{"id":"https://openalex.org/W4412376982","doi":"https://doi.org/10.1145/3726302.3730188","title":"ELEC: Efficient Large Language Model-Empowered Click-Through Rate Prediction","display_name":"ELEC: Efficient Large Language Model-Empowered Click-Through Rate Prediction","publication_year":2025,"publication_date":"2025-07-13","ids":{"openalex":"https://openalex.org/W4412376982","doi":"https://doi.org/10.1145/3726302.3730188"},"language":"en","primary_location":{"id":"doi:10.1145/3726302.3730188","doi":"https://doi.org/10.1145/3726302.3730188","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3726302.3730188","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3726302.3730188","source":{"id":"https://openalex.org/S4363608773","display_name":"International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 48th International ACM SIGIR Conference on Research and Development in Information Retrieval","raw_type":"proceedings-article"},"volume":null,"type":"conference-paper","indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"bronze","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3726302.3730188","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5027582764","display_name":"Rui Dong","orcid":"https://orcid.org/0000-0002-8276-5749","observed_orcids":["https://orcid.org/0000-0002-8276-5749"]},"institutions":[{"id":"https://openalex.org/I45928872","display_name":"Alibaba Group (China)","ror":"https://ror.org/00k642b80","country_code":"CN","type":"company","lineage":["https://openalex.org/I45928872"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Rui Dong","raw_affiliation_strings":["Alibaba Group, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0002-8276-5749","affiliations":[{"raw_affiliation_string":"Alibaba Group, Beijing, China","institution_ids":["https://openalex.org/I45928872"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5109229971","display_name":"Robin Wentao Ouyang","orcid":"https://orcid.org/0000-0002-8704-043X","observed_orcids":["https://orcid.org/0000-0002-8704-043X"]},"institutions":[{"id":"https://openalex.org/I45928872","display_name":"Alibaba Group (China)","ror":"https://ror.org/00k642b80","country_code":"CN","type":"company","lineage":["https://openalex.org/I45928872"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wentao Ouyang","raw_affiliation_strings":["Alibaba Group, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0002-8704-043X","affiliations":[{"raw_affiliation_string":"Alibaba Group, Beijing, China","institution_ids":["https://openalex.org/I45928872"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5056480694","display_name":"Xiangzheng Liu","orcid":"https://orcid.org/0009-0005-1095-3918","observed_orcids":["https://orcid.org/0009-0005-1095-3918"]},"institutions":[{"id":"https://openalex.org/I45928872","display_name":"Alibaba Group (China)","ror":"https://ror.org/00k642b80","country_code":"CN","type":"company","lineage":["https://openalex.org/I45928872"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiangzheng Liu","raw_affiliation_strings":["Alibaba Group, Beijing, China"],"raw_orcid":"https://orcid.org/0009-0005-1095-3918","affiliations":[{"raw_affiliation_string":"Alibaba Group, Beijing, China","institution_ids":["https://openalex.org/I45928872"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I45928872"],"apc_list":null,"apc_paid":null,"fwci":2.3275,"has_fulltext":true,"cited_by_count":2,"citation_normalized_percentile":{"value":0.88829233,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"2664","last_page":"2668"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10203","display_name":"Recommender Systems and Techniques","score":0.5425000190734863,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10203","display_name":"Recommender Systems and Techniques","score":0.5425000190734863,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10286","display_name":"Information Retrieval and Search Behavior","score":0.42969998717308044,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12101","display_name":"Advanced Bandit Algorithms Research","score":0.0027000000700354576,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/click-through-rate-prediction","display_name":"click-through rate prediction","score":1.0},{"id":"https://openalex.org/keywords/large-language-models","display_name":"large language models","score":0.9629999995231628},{"id":"https://openalex.org/keywords/online-advertising","display_name":"online advertising","score":0.9240000247955322},{"id":"https://openalex.org/keywords/pseudo-siamese-network","display_name":"pseudo-siamese network","score":0.9660000205039978},{"id":"https://openalex.org/keywords/knowledge-distillation","display_name":"knowledge distillation","score":0.9399999976158142},{"id":"https://openalex.org/keywords/tabular-data","display_name":"tabular data","score":0.6589999794960022},{"id":"https://openalex.org/keywords/feature-interaction-modeling","display_name":"feature interaction modeling","score":0.6840000152587891},{"id":"https://openalex.org/keywords/text-representation","display_name":"text representation","score":0.4740000069141388},{"id":"https://openalex.org/keywords/inference-efficiency","display_name":"inference efficiency","score":0.7260000109672546},{"id":"https://openalex.org/keywords/model-agnostic-framework","display_name":"model-agnostic framework","score":0.5559999942779541}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7313045263290405},{"id":"https://openalex.org/C115174607","wikidata":"https://www.wikidata.org/wiki/Q1100934","display_name":"Click-through rate","level":2,"score":0.518508791923523},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.5118376612663269},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.35231560468673706},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.33158227801322937},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3307472765445709},{"id":"https://openalex.org/C136764020","wikidata":"https://www.wikidata.org/wiki/Q466","display_name":"World Wide Web","level":1,"score":0.10818007588386536}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1145/3726302.3730188","doi":"https://doi.org/10.1145/3726302.3730188","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3726302.3730188","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3726302.3730188","source":{"id":"https://openalex.org/S4363608773","display_name":"International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 48th International ACM SIGIR Conference on Research and Development in Information Retrieval","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2509.07594","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2509.07594","pdf_url":"https://arxiv.org/pdf/2509.07594","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2509.07594","doi":"https://doi.org/10.48550/arxiv.2509.07594","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2509.07594","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.1145/3726302.3730188","doi":"https://doi.org/10.1145/3726302.3730188","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3726302.3730188","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3726302.3730188","source":{"id":"https://openalex.org/S4363608773","display_name":"International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 48th International ACM SIGIR Conference on Research and Development in Information Retrieval","raw_type":"proceedings-article"},"sustainable_development_goals":[],"sustainable_development_goals_aurora":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4412376982.pdf","grobid_xml":"https://content.openalex.org/works/W4412376982.grobid-xml"},"referenced_works_count":28,"referenced_works":["https://openalex.org/W2076618162","https://openalex.org/W2090883204","https://openalex.org/W2295739661","https://openalex.org/W2443960221","https://openalex.org/W2475334473","https://openalex.org/W2517540742","https://openalex.org/W2548570154","https://openalex.org/W2604662567","https://openalex.org/W2723293840","https://openalex.org/W2743209601","https://openalex.org/W2788490371","https://openalex.org/W2793768763","https://openalex.org/W2898085636","https://openalex.org/W2948903560","https://openalex.org/W2962745591","https://openalex.org/W2964182926","https://openalex.org/W3032044946","https://openalex.org/W3093945404","https://openalex.org/W4296591867","https://openalex.org/W4367191003","https://openalex.org/W4384625597","https://openalex.org/W4384648324","https://openalex.org/W4386728933","https://openalex.org/W4392384238","https://openalex.org/W4400909953","https://openalex.org/W4401863700","https://openalex.org/W4401863964","https://openalex.org/W4403221563"],"related_works":["https://openalex.org/W4286419063","https://openalex.org/W2011720192","https://openalex.org/W2169518243","https://openalex.org/W3189010682","https://openalex.org/W4231937131","https://openalex.org/W3188962172","https://openalex.org/W323219885","https://openalex.org/W2063928587","https://openalex.org/W2772917594","https://openalex.org/W3204019825"],"abstract_inverted_index":{"Click-through":[0],"rate":[1],"(CTR)":[2],"prediction":[3,18,97,107],"plays":[4],"an":[5,93,102],"important":[6],"role":[7],"in":[8,24,35,46,58],"online":[9],"advertising":[10],"systems.":[11],"On":[12,37],"the":[13,21,38,48,75,105,113,116,124,138,144,153,173,181,184,188,193,197,202,218,228,243],"one":[14],"hand,":[15,40],"traditional":[16],"CTR":[17,96,106,149,236,250],"models":[19,81],"capture":[20],"collaborative":[22,60,148,235],"signals":[23,61],"tabular":[25,164,207],"data":[26,208],"via":[27],"feature":[28],"interaction":[29],"modeling,":[30],"but":[31,54,118,211],"they":[32,55,63],"lose":[33],"semantics":[34,85],"text.":[36],"other":[39],"Large":[41],"Language":[42],"Models":[43],"(LLMs)":[44],"excel":[45],"understanding":[47],"context":[49],"and":[50,62,82,86,132,166,196,234,245],"meaning":[51],"behind":[52],"text,":[53],"face":[56],"challenges":[57],"capturing":[59],"have":[64],"long":[65],"inference":[66],"latency.":[67],"In":[68,109],"this":[69],"paper,":[70],"we":[71,122],"aim":[72],"to":[73,111,151,187],"leverage":[74,112],"benefits":[76],"of":[77,80,115,162,247],"both":[78,163,192],"types":[79],"pursue":[83],"collaboration,":[84],"efficiency.":[87,177],"We":[88,99,136,178],"present":[89],"ELEC,":[90],"which":[91,127],"is":[92,223],"Efficient":[94],"LLM-Empowered":[95],"framework.":[98],"first":[100],"adapt":[101],"LLM":[103,117,145,174],"for":[104,227,249],"task.":[108],"order":[110],"ability":[114],"simultaneously":[119],"keep":[120],"efficiency,":[121],"utilize":[123],"pseudo-siamese":[125],"network":[126,131,155,186,190,204],"contains":[128],"a":[129,133,147],"gain":[130,154,185,219],"vanilla":[134,189,203],"network.":[135,220],"inject":[137],"high-level":[139],"representation":[140,198],"vector":[141],"generated":[142],"by":[143],"into":[146],"model":[150],"form":[152],"such":[156,200],"that":[157,201],"it":[158],"can":[159,212],"take":[160],"advantage":[161],"modeling":[165],"textual":[167],"modeling.":[168],"However,":[169],"its":[170,176],"reliance":[171],"on":[172,191,239],"limits":[175],"then":[179],"distill":[180],"knowledge":[182],"from":[183],"score":[194],"level":[195],"level,":[199],"takes":[205],"only":[206],"as":[209,217],"input,":[210],"still":[213],"generate":[214],"comparable":[215],"performance":[216],"Our":[221],"approach":[222],"model-agnostic.":[224],"It":[225],"allows":[226],"integration":[229],"with":[230],"various":[231],"existing":[232],"LLMs":[233],"models.":[237],"Experiments":[238],"real-world":[240],"datasets":[241],"demonstrate":[242],"effectiveness":[244],"efficiency":[246],"ELEC":[248],"prediction.":[251]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":1}],"updated_date":"2026-10-16T09:59:22.235173","created_date":"2025-10-10T00:00:00"}